标签：python算法
一.无序表查找 def sequential_search(lis, key): for i in lis: if i == key: return lis.index(i) else: continue else: return False if __name__ == '__main__': LIST = [1, 5, 8, 123, 22, 54, 7, 99, 300, 222] result = sequential_search(LIST,1231) print(result) 二.有序表查找 1.二分查找(Binary Search) def binary_search(lis,key): low = 0 high = len(lis) - 1 time = 0 while low < high: time += 1 mid = int((low + high)/2) if key < lis[mid]: high = mid - 1 elif key > lis[mid]: low = mid + 1 else: print("times: %s" % time) return mid print("times: %s" % time) return False if __name__ == '__main__': LIST = [1, 5, 7, 8, 22, 54, 99, 123, 200, 222, 444] result = binary_search(LIST,99) print(result) 2. 插值查找 二分查找法虽然已经很不错了，但还有可以优化的地方。 有的时候，对半过滤还不够狠，要是每次都排除十分之九的数据岂不是更好？选择这个值就是关键问题，插值的意义就是：以更快的速度进行缩减。 插值的核心就是使用公式： value = (key – list[low])/(list[high] – list[low]) 用这个value来代替二分查找中的1/2 def binary_search(lis, key): low = 0 high = len(lis) - 1 time = 0 while low < high: time += 1 # 计算mid值是插值算法的核心代码 mid = low + int((high - low) * (key - lis[low]) / (lis[high] - lis[low])) print("mid=%s, low=%s, high=%s" % (mid, low, high)) if key < lis[mid]: high = mid - 1 elif key > lis[mid]: low = mid + 1 else: # 打印查找的次数 print("times: %s" % time) return mid print("times: %s" % time) return False标签：python算法
原文地址：http://blog.51cto.com/haoyonghui/2055623
